Parkinsonism is a syndrome that occurs as a combination of six cardinal signs; resting tremor, rigidity, bradykinesia, loss of postural reflex, flexion posture and freezing (motor block). Parkinson disease occurs with the loss of brain cells which are generating dopamine. The most important primary motor symptoms of Parkinson's disease are shaking of hands, slowness of movement, and speech changes. Sound changes are not recognized at the early stages of the disease while it becomes evident at the progressive stages. However, speech changes can be detected with some acoustic parameters. This study aims to detect Parkinson's disease by using voice recordings. In this study, 342 voice recordings that belong to 174 healthy subjects and 168 Parkinson's disease patients are used. 21 features are extracted from each voice record. The classification of subjects as healthy or with Parkinson disease is achieved by using logistic regression, k-nearest neighboring and ensemble gentle boost techniques. Furthermore, tenfold and leave-one-out cross validation techniques are applied to improve the performance and reliability of the classifier. Sensitivity, specificity, maximum and average accuracy values are calculated to evaluate the success of the system. The obtained results show that the proposed system can be utilized by the neurologists to diagnose Parkinson's disease at its early stages.